Time varying density of tissues

ABSTRACT

Systems for providing time varying density signatures of tissues are presented. The time varying density signatures can be calculated based on obtained sensor data reflecting one or more properties (e.g., physical, mechanical, electrical, etc.) of a target tissue. The time varying density signatures can then be used to generate an output where the output indicates how a target tissue&#39;s density changes with respect to time, possibly based on first, second, third, or higher order derivatives with respect to time.

This application claims priority to U.S. provisional applications having serial numbers:

-   -   61/529,109, 61/528,949, and 61/528,984, filed Aug. 30, 2011;     -   61/529,556, and 61/529,610, filed Aug. 31, 2011;     -   61/532,923, 61/532,944, and 61/532,988 filed Sep. 9, 2011; and     -   61/543,644 filed Oct. 5, 2011.

These and all other extrinsic materials discussed herein are incorporated by reference in their entirety. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

FIELD OF THE INVENTION

The field of the invention is tissue density detection technologies.

BACKGROUND

In medical imaging, volumetric data sets are frequently assessed by rendering 3-dimensional (3D) models with which a user can interact to view, measure, or segment specific anatomical structure or tissues. It is common to acquire multiple volumes of the same patient data, either with the same modality (e.g., CT) in a time-specific manner, or from different modalities. Frequently, the intent is to compare one volumetric data set to another. To facilitate the comparison the datasets may be registered in where one dataset is aligned with the other. In a rigid registration, voxel information of each data set is left unchanged (FIG. 1), but the data can be adjusted by translation, rotation, and/or scale. Because of physiological movement, biological growth and the inherent differences in each acquisition, tissue properties, environmental characteristics or other issue, the anatomy frequently does not align through rigid registration, but requires morphological adjustments of one data set to another. Non-rigid, or deformable, registration is performed by aligning specific targets in one data set to another and then mapping the tissue from a position (e.g., pixel, voxel) in one data set to its position in the subsequent data sets in a transform (FIG. 2).

With deformable registration as the substrate, living tissues can be imaged and quantified to offer a healthcare provider new quantization of tissue characteristics. Previous techniques based on rigid registration do not track the tissue's change in position, or other tissue properties, from time-point to time-point.

Interestingly, previous techniques based on rigid registration fail to provide sufficient information for true three dimensional motions of a tissue especially when the tissue has a time varying tissue characteristic such as tissue density. Thus there is still a need for providing motion data of a tissue that reflects a time varying tissue characteristic.

Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods in which a signature of tissue density can be measured. One aspect of the inventive subject matter includes a system for providing information about a tissue. The contemplated system directs incident energy (e.g., ultrasound, RF, MRI, CT, X-ray, etc.) to a target tissue while a sensor collects signals from interactions between the tissue and energy. The system can further include a signal converter that converts the signals into a time varying density signature of the tissue, which can be rendered on an output device (e.g., a display, a monitor, a printer, a database, etc.). The collected signals can also be selectively received (e.g., filtered, focused, directed, etc.) to focus on a specific signal or portion of the tissue. Further, the sensor could be distal from the tissue, physically touching the tissue, or possibly implanted into the tissue.

Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic of voxel information remaining unchanged through rigid registration.

FIG. 2 is a schematic of voxel information changing through non-rigid registration.

FIG. 3 is a schematic of a system that determines a time varying density signature of a target tissue.

FIG. 4 illustrates an aortic value imaged using deformable registration.

DETAILED DESCRIPTION

It should be noted that while the following description is drawn to a computer/server based tissue analysis system, various alternative configurations are also deemed suitable and may employ various computing devices including servers, interfaces, systems, databases, agents, peers, engines, controllers, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). The software instructions preferably configure the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. In especially preferred embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges preferably are conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network.

One should appreciate that the disclosed techniques provide many advantageous technical effects including bathing a target tissue in energy and converting returned tissue signals into a time varying density signature corresponding to the tissue. A signal converter leverages the signature to configure an output device to present information related to the signature.

The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within this document the terms “coupled to” and “coupled with” are also used euphemistically to mean “communicatively coupled with” where two or more networked devices are able to communicate over a network.

Qi Imaging, LLC (formally Ziosoft Inc; see www.ziosoftinc.com) has pioneered systems and methods for deformable registration as described in the following issued patents and published patent applications. The disclosed techniques build upon these foundational works:

-   -   U.S. Pat. No. 7,310,095; U.S. Pat. No. 7,420,575; U.S. Pat. No.         7,424,140; U.S. Pat. No. 7,502,025; U.S. Pat. No. 7,529,396;         U.S. Pat. No. 7,574,027; U.S. Pat. No. 7,576,741; U.S. Pat. No.         7,616,205; U.S. Pat. No. 7,620,224; U.S. Pat. No. 7,623,695;         U.S. Pat. No. 7,639,855; U.S. Pat. No. 7,639,867; U.S. Pat. No.         7,647,593; U.S. Pat. No. 7,653,231; U.S. Pat. No. 7,689,018;         U.S. Pat. No. 7,706,588; U.S. Pat. No. 7,738,701; U.S. Pat. No.         7,778,451; U.S. Pat. No. 7,782,507; U.S. Pat. No. 7,796,835;         U.S. Pat. No. 7,817,877; U.S. Pat. No. 7,825,924; U.S. Pat. No.         7,853,057; U.S. Pat. No. 7,860,284; U.S. Pat. No. 7,860,949;         U.S. Pat. No. 7,869,638; U.S. Pat. No. 7,873,197; U.S. Pat. No.         7,907,763;

and

-   -   U.S. 2006/0155800; U.S. 2007/0223832; U.S. 2008/0075346; U.S.         2008/0101672; U.S. 2008/0136815; U.S. 2008/0170768; U.S.         2008/0297509; U.S. 2009/0003668; U.S. 2009/0019400; U.S.         2009/0119609; U.S. 2009/0129642; U.S. 2009/0174729; U.S.         2009/0290769; U.S. 2010/0007663; U.S. 2010/0142788; U.S.         2011/0075888; U.S. 2011/0075896; WO 2011/037853; WO 2011/037860.

Registration is a standard image processing technique. Many algorithms and approaches are used to accomplish both rigid and deformable registration. This document presents the concept of automating the registration process to allow for better correlation between serially-acquired data sets or multi-modality datasets. In automating the registration process and deriving a desired transform, automated metrics can be calculated between registered voxels, such that voxel-to-voxel analysis can be performed; tissues can be segmented; time-based changes can be automated.

FIG. 3 presents system 100 for providing information about a tissue. System 100 can include several different components that aid in determining time varying density signature 145 associated with tissue 120.

Tissue 120 can include various forms of tissues associated with a patient (e.g., human, animal, etc.). Preferably tissue 120 represents at least a portion of larger anatomical structure, possibly an organ. For example, tissue 120 can include an organ that has a time varying density, possibly a heart, lung, trachea, bladder, valve, or other type of organ. Typically, as time passes tissue 120 changes shape or dimension (e.g., length, depth, width, cross sectional area, volume, etc.) The disclosed techniques provide an infrastructure that observes how a density associated with tissue 120 changes as tissue 120 changes shape or dimension.

System 100 can include one or more of device 110 configured to emit indecent engine 115 on tissue 120. In response to incident energy 115 interacting with tissue 120, tissue 120 emits tissue signal 125. Tissue signal 125 can include a reflected signal, an absorption signal, a transmission signal, or other type of signal originating from tissue 120 due to incident energy 115. For example, incident energy 115 could include X-Rays and tissue signal 125 could include information indicating transmission (or absorption) of X-Rays due to tissue 120. Example incident energies can include ultrasound (i.e., acoustic energy), X-Rays, magnetic resonance energy, RF energy (i.e., electromagnetic energy), neutrons (i.e., particle energy), or other forms of energy.

Device 110 can take on many different forms. Example devices can include one or more of Magnetic Resonance Imaging (MRI) machine, a computerized tomography (CT) machine, and ultrasound machine, nuclear imaging (e.g., positron emission tomography (PET), single photon emission computer tomography (SPECT), molecular imaging, and an x-ray machine. Naturally, incident energy 115 and tissue signal 125 depend on the nature of the device.

System 100 further includes one or more sensors 130 configured to receive tissue signal 125 resulting, at least in part, from interactions of incident energy 115 with tissue 120. Sensors 130 can also take on many different forms, preferably a form that is commensurate with the nature of tissue signal 125. For example, when tissue signal 120 represents acoustic energy, sensors 130 can include one or more acoustic transducers. In embodiments where tissue signal 120 comprises electromagnetic energy, sensors 120 can comprises antennas, magnetic, CCDs, or other electromagnetic sensors.

In some embodiments, sensors 130 are disposed proximate to tissue 120. For example, when tissue signal 125 includes acoustic energy, sensors 130 (e.g., piezoelectric transducers) can be in physical contact with a patient or with tissue 120. In other embodiments, sensors 130 can be placed distal from tissue 120 (i.e., not physically touching the patient or tissue). For example, in an embodiment where device 110 comprises MRI system, the sensor does not necessarily have to touch tissue 120.

Sensor 130 collects data associated with tissue 130 and provide the data to signal converter 140. Signal converter 140, preferably a computing device coupled with sensor 130 or possibly device 110, converts tissue signal 125 into time varying density signature 145. In some embodiment, signal convertor 140 monitors tissue signal 125 over time to observe how tissue signal 125 changes with time, possibly representing changes in shape, dimension, mass, or other tissue characteristic. Consider a scenario of monitoring lung function of a patient. Tissue signal 125 can represent physical dimensions of the patient's lung. Signal converter 140 can use the information to calculate a volume of the lung as the patient continues respiration. During inhalation, time varying density signature 145 of the lung will decrease because the volume of the lung increases while lung mass (i.e., mass of the lung tissue being monitored) remains substantially constant. During exhalation, time varying density signature 145 of the lung will increase due to reduction in volume of the lungs. One should also appreciate other factors can be at play giving rise to time varying density signature 145 with respect to anatomical structures such as the lung. To continue the example, time varying density signature 145 of the lung changes due to respiration, but can also vary due to changes in blood flow through the lung, which can affect mass of tissue 120. Thus, the disclosed techniques measure changes in physical properties of tissue 120 as well as other prosperities. Other properties can include tissue mass or even non-mass properties. Example non-mass properties can include electrical properties (e.g., resistance, conductivity, inductance, etc.), chemical properties (e.g., p.H., oxygen-saturation, biological properties (e.g., innervation, muscle type, vascularity, etc.), mechanical properties (e.g., stress, strain, shear, elasticity, hardness, etc.), tissue state (e.g., necrotic, living, etc.), or other types of tissue properties. The properties of tissues 120 can be derived directly or indirectly from the sensor data. For example, when tissue signal 120 includes acoustic data, the acoustic data can be representative of mechanical properties of tissue 120. When tissue signal 120 includes electromagnetic data, the electromagnetic data can represent electrical properties or possibly chemical properties of tissue 120.

One should appreciate that time varying density signature 145 represents a time varying ratio of two properties, rather than only representing a mass-based density. For example, a density (δ) can include a ratio of a property per unit length, per unit area, or even per unit volume. Using mass as an example, density can simply include a mass of tissue 120 divided by the volume associated with tissue 120 as calculated by signal converter 140. Still, a density could include conductivity per unit length, stress or strain per unit area, or other type of property density.

Time varying density signature 145 further includes a time varying nature of the tissue property density, which can be expressed as a derivative dδ/dt or difference Δδ/Δt. The latter can be calculated more readily by signal converter 140 at multiple time intervals or phases. One should further note that the time varying nature of density can also depend on higher order time derivatives. Thus, the inventive subject matter is considered to include calculating first (46/40, second (Δ²δ/Δt²), third (Δ³δ/Δt³), forth (Δ⁴δ/Δt⁴), or higher order time “derivatives” of density. Such higher order representations of density provide additional diagnostic value. Consider a scenario when a patient convulses. The convulsions of tissues can have specific higher order signatures that might not be apparent through simple static imaging. Once the signature is analyzed, the signature can be compared to known signatures to determine a type or nature of affliction.

Although FIG. 3 illustrates device 110, sensors 130, and signal converter 140 as distinct apparatus, one should appreciate that all three of the components can be integrated together as a single system or apparatus. For example, the three components can be integrated into an MRI system, an ultrasound machine, or possibly a hybrid of known systems.

Sensors 130 can be further configured, possibly under command of signal converter 140, to selectively receive tissue signal 125 from tissue 120. For example, sensors 130 can include filters to select desired characteristics of tissue signal 125 (e.g., frequency, amplitude, phase, polarization, etc.). Further, sensors 130 can be configured to selectively target an anatomical structure that comprises tissue 120. In such embodiments, sensors 130 can filter tissue signal 125 from other regions of the patient except signals origination from a desired anatomical structure (e.g., heart, brain, lungs, bladder, liver, muscle, etc.). Such filtering can occur at sensor 130, signal converter 140, or possibly at output device 150. Such filtering or selectivity of tissue signal 125 is considered advantageous especially when the anatomical structure comprises a fractural property or geometry; a lung or portions of the circulatory system for example.

Upon generation, time varying density signature 145 can be transmitted, possibly over a network, to output device 150. Output device 150 preferably produces output 156 that depends on time varying density signature 145. Output 156 is illustrated as graph showing density as a function of time, possibly representing lung density through multiple respiration cycles.

In more preferred embodiments, output 156 can include an image of tissue 120 that can be rendered on display 155 of output device 150. The image of the tissue can include an overlay illustrating the time varying nature of density. For example, the overlay can include contours, colors, vectors, or other enhancements illustrating how the density signature varies with time. More preferably, the image itself can be time varying. Consider a scenario of imaging a heart while it beats. Output device 150 can display a time varying video of the beating heart and include an overlay illustrating how the mass density of the heart is affected in time. In more preferred embodiments, output 156 can be generated in real-time. For more complex renderings, output 156 can be generated offline, possibly via a rendering could-based service. One should further appreciate the disclosed techniques allow for digitally or virtual viewing cross sections of tissue 120 or its associated anatomical structure. Therefore, output 156 can include representations of density changes in time with respect to a cross section. Such an approach is considered advantageous because a density of tumor or scar tissue within tissue 120 would likely not change with time.

Output 156 can be generated through various techniques. In some embodiments, output 156 can include an image generated through known rigid registration techniques. In more preferred embodiments, output 156 includes a deformable registration image of tissue 120 generated based on the techniques pioneered by Qi Imaging as referenced above. Further, time varying density signature 145 can be represented on a voxel-by-voxel basis, especially based on deformable registration. A voxel-by-voxel representation gives rise to in depth volumetric analysis of tissue density variations by length, area, volume or by various time derivatives. Thus output 156 can include a fine grain representation of time varying density signature 145.

Rigid Registration: SPECT/CT Cardiac Imaging

Coronary artery disease (CAD) is assessed based on both anatomical and physiological data. Clinicians may use two discrete modalities to evaluate the anatomical and physiological component of CAD. A CT exam may be used to provide anatomical information of the coronary arteries and whether stenosis exist to impede blood flow. A SPECT scan (or stress test) is used to measure the perfusion of blood flow to the heart muscle. These two examples provide complementary information and it therefore, is useful to view the information contained in each exam in relationship to each other. A rigid registration or deformable registration method can be used to align the position of the left ventricle in the SPECT image to that of the CT image. This allows for simultaneous viewing of the perfusion map to the coronary artery distribution to determine if blockages in the anatomy are related to deficits in perfusion.

Deformable Registration: Respiratory Analysis of Lung

A time varying density signature can cover a broad spectrum of data representations. In one especially contemplated use case, the time varying density signature includes an image data of the lung, of other fractal anatomical structure, where the image data comprises one or more highlights showing the density of the lung tissue or cavities as function of time. The lungs can be imaged in various stages of the respiratory cycle, e.g., inspiration through expiration. With CT imaging, the voxel information for the same target tissue of the lung will decrease in density as more air is averaged into the voxel, thus the value per voxel should be lower in the inspired lung relative to the expired lung. Registered tissue voxels can be compared between the inspired and expired lung volumes, or at any stage in between, to determine specific characteristics within individual lobes or even individual voxels to characterize the specific function at a voxel level. Further the density signature can be characterized by changes in time (e.g., first, second, third or higher order time derivatives of density). Thus the tissue's density signature can be presented as a time varying image.

Deformable Registration: TAVI

Transcatheter aortic valve implantation allows surgeons to replace calcified aortic valves through a catheter approach. A CT exam is performed for pre-operative planning to determine sizing and placement of the prosthetic valve. A critical measurement is to determine the aortic valve annulus dimensions (e.g., area, circumference). The annulus dimensions will change throughout the cardiac cycle. If a 10-phase cardiac CT scan is deformable registered, the annulus can be measured in one phase and then through automation using the transforms the annulus dimensions will be determined through the cardiac cycle by propagating the specified dimensions of interest across the registered phases. This will automatically generate maximum and minimum values through the cardiac cycle. An aortic valve is illustrated in FIG. 4.

The concept of time varying density includes time-based changes to mass per unit volume. Still, the concept is further considered to include other types of density including a value of a tissue property per unit area or unit length. For example, a valve orifice can have a calcification-level per unit area. Therefore, density can be a measure of a quantity per unit length, area, volume, angular displacement, or other type of density include mass related density or non-mass related density.

It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C . . . and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc. 

What is claimed is:
 1. A system for providing information about a tissue, comprising: a device that provides energy to the tissue; a sensor configured to receive a signal from that tissue that results at least in part from interaction of the energy with the tissue; a signal converter coupled with the sensor that converts the signal to a time varying density signature corresponding to a portion of the tissue; and an output device that produces an output dependent upon the density signature.
 2. The system of claim 1 wherein the device is selected from the group consisting of a Magnetic Resonance Imaging (MRI) machine, a computerized tomography (CT) machine, an ultrasound machine, nuclear imaging (e.g., positron emission tomography (PET), single photon emission computer tomography (SPECT), molecular imaging, and an x-ray machine.
 3. The system of claim 1 wherein the sensor is disposed distally from the tissue.
 4. The system of claim 1 wherein the sensor is physically touching the tissue.
 5. The system of claim 1 wherein the sensor is configured to selectively receive the signal from the tissue.
 6. The system of claim 5, wherein the sensor is configured to selectively receive the signal from an anatomical structure comprising the tissue.
 7. The system of claim 6, wherein the anatomical structure is a lung.
 8. The system of claim 6, wherein the anatomical structure comprises a fractal property.
 9. The system of claim 1, wherein the output device comprises a display.
 10. The system of claim 9, wherein the display is configured to present an image as the output dependent upon the density signature.
 11. The system of claim 9, wherein the image is a time varying image.
 12. The system of claim 9, wherein the output comprises a deformation registration image of the tissue.
 13. The system of claim 9, wherein the output comprises a rigid registration image of the tissue.
 14. The system of claim 1, wherein the output is generated in real-time.
 15. The system of claim 1, wherein the time varying density signature comprises at least a length measurement of the tissue.
 16. The system of claim 15, wherein the time varying density signature comprises at least an area measurement of the tissue.
 17. The system of claim 16, wherein the time varying density signature comprises at least a volume measurement of the tissue.
 18. The system of claim 1, wherein the time varying density signature represents a non-mass property of the tissue. 